solo-epd-loader
===============
|pypi Version| |conda version| |python version| |pytest| |codecov| |license| |zenodo doi|
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Python data loader for Solar Orbiter's (SolO) `Energetic Particle Detector (EPD) <http://espada.uah.es/epd/>`_. At the moment provides level 2 (l2) and low latency (ll) data (`more details on data levels here <http://espada.uah.es/epd/EPD_data_overview.php>`_) obtained through CDF files from ESA's `Solar Orbiter Archive (SOAR) <http://soar.esac.esa.int/soar>`_ for the following sensors:
- Electron Proton Telescope (EPT)
- High Energy Telescope (HET)
- SupraThermal Electrons and Protons (STEP)
Current caveats:
- Only the standard ``rates`` data products are supported (i.e., no ``burst`` or ``high cadence`` data).
- For EPT and HET, only electrons, protons and alpha particles are processed (i.e., for HET He3, He4, C, N, O, Fe are omitted at the moment).
- For STEP, electron data needs to be calculated manually.
- The Suprathermal Ion Spectrograph (SIS) is not yet included.
Disclaimer
----------
This software is provided "as is", with no guarantee. It is no official data source, and not officially endorsed by the corresponding instrument teams. **Please always refer to the** `official EPD data description <http://espada.uah.es/epd/EPD_data.php>`_ **before using the data!**
Installation
------------
solo_epd_loader requires python >= 3.6.
It can be installed either from `PyPI <https://pypi.org/project/solo-epd-loader/>`_ using:
.. code:: bash
pip install solo-epd-loader
or from `Anaconda <https://anaconda.org/conda-forge/solo-epd-loader/>`_ using:
.. code:: bash
conda install -c conda-forge solo-epd-loader
Usage
-----
The standard usecase is to utilize the ``epd_load`` function, which
returns Pandas dataframe(s) of the EPD measurements and a dictionary
containing information on the energy channels.
.. code:: python
from solo_epd_loader import epd_load
df_1, df_2, energies = epd_load(sensor, startdate, enddate=None, level='l2', viewing=None, path=None,
autodownload=False, only_averages=False)
Input
~~~~~
- ``sensor``: ``'ept'``, ``'het'``, or ``'step'`` (string)
- ``startdate``, ``enddate``: Datetime object (e.g., ``dt.date(2021,12,31)`` or ``dt.datetime(2021,4,15)``) or integer of the form yyyymmdd with empty positions filled with zeros, e.g. ``20210415`` (if no ``enddate`` is provided, ``enddate = startdate`` will be used)
- ``level``: ``'l2'`` or ``'ll'`` (string); defines level of data product: level 2 (``'l2'``) or low-latency (``'ll'``). By default ``'l2'``.
- ``viewing``: ``'sun'``, ``'asun'``, ``'north'``, ``'south'``, ``'omni'`` (string) or ``None``; not
needed for ``sensor = 'step'``. ``'omni'`` is just calculated as the average of the other four viewing directions: ``('sun'+'asun'+'north'+'south')/4``
- ``path``: directory in which Solar Orbiter data is/should be
organized; e.g. ``'/home/userxyz/solo/data/'`` (string). See `Data folder structure`_ for more details.
- ``autodownload``: if ``True``, will try to download missing data files
from SOAR (bolean)
- ``only_averages``: If ``True``, will for STEP only return the averaged fluxes, and not the data of each of the 15 Pixels. This will reduce the memory consumption. By default ``False``.
Return
~~~~~~
- For ``sensor`` = ``'ept'`` or ``'het'``:
1. Pandas dataframe with proton fluxes and errors (for EPT also alpha
particles) in ‘particles / (s cm^2 sr MeV)’
2. Pandas dataframe with electron fluxes and errors in ‘particles /
(s cm^2 sr MeV)’
3. Dictionary with energy information for all particles:
- String with energy channel info
- Value of lower energy bin edge in MeV
- Value of energy bin width in MeV
- For ``sensor`` = ``'step'``:
1. Pandas dataframe with fluxes and errors in ‘particles / (s cm^2 sr
MeV)’
2. Dictionary with energy information for all particles:
- String with energy channel info
- Value of lower energy bin edge in MeV
- Value of energy bin width in MeV
SupraThermal Electron Proton (STEP) sensor electron measurements
----------------------------------------------------------------
Please note that the STEP electron measurements are not directly provided in the publically released data, but need to be calculated from them. This process is not straightforward, and the resulting data is prone to uncertainties (like contamination). **Thus it should only be used scientifically with caution! Please refer to the** `official EPD data description <http://espada.uah.es/epd/EPD_data.php>`_ **before using the data!**
Data folder structure
---------------------
The ``path`` variable provided to the module should be the base
directory where the corresponding cdf data files should be placed in
subdirectories. First subfolder defines the data product ``level``
(``l2`` or ``low_latency`` at the moment), the next one the
``instrument`` (so far only ``epd``), and finally the ``sensor``
(``ept``, ``het`` or ``step``).
For example, the folder structure could look like this:
``/home/userxyz/solo/data/l2/epd/het``. In this case, you should call
the loader with ``path='/home/userxyz/solo/data'``; i.e., the base
directory for the data.
You can use the (automatic) download function described in the following
section to let the subfolders be created initially automatically. NB: It might
be that you need to run the code with *sudo* or *admin* privileges in order to
be able to create new folders on your system.
Data download within Python
---------------------------
While using ``epd_load()`` to obtain the data, one can choose to automatically
download missing data files from `SOAR <http://soar.esac.esa.int/soar>`_
directly from within python. They are saved in the folder provided by the
``path`` argument (see above). For that, just add ``autodownload=True`` to the
function call:
.. code:: python
from solo_epd_loader import epd_load
df_protons, df_electrons, energies = \
epd_load(sensor='het', level='l2', startdate=20200820,
enddate=20200821, viewing='sun',
path='/home/userxyz/solo/data/', autodownload=True)
# plot protons and alphas
ax = df_protons.plot(logy=True, subplots=True, figsize=(20,60))
plt.show()
# plot electrons
ax = df_electrons.plot(logy=True, subplots=True, figsize=(20,60))
plt.show()
Note: The code will always download the *latest version* of the file
available at SOAR. So in case a file ``V01.cdf`` is already locally
present, ``V02.cdf`` will be downloaded nonetheless.
Example 1 - low latency data
----------------------------
Example code that loads low latency (ll) electron and proton (+alphas)
fluxes (and errors) for EPT NORTH telescope from Apr 15 2021 to Apr 16
2021 into two Pandas dataframes (one for protons & alphas, one for
electrons). In general available are ‘sun’, ‘asun’, ‘north’, ‘south’, and ‘omni’
viewing directions for ‘ept’ and ‘het’ telescopes of SolO/EPD.
.. code:: python
from matplotlib import pyplot as plt
from solo_epd_loader import epd_load
df_protons, df_electrons, energies = \
epd_load(sensor='ept', level='ll', startdate=20210415,
enddate=20210416, viewing='north',
path='/home/userxyz/solo/data/')
# plot protons and alphas
ax = df_protons.plot(logy=True, subplots=True, figsize=(20,60))
plt.show()
# plot electrons
ax = df_electrons.plot(logy=True, subplots=True, figsize=(20,60))
plt.show()
Example 2 - level 2 data
------------------------
Example code that loads level 2 (l2) electron and proton (+alphas)
fluxes (and errors) for HET SUN telescope from Aug 20 2020 to Aug 20
2020 into two Pandas dataframes (one for protons & alphas, one for
electrons).
.. code:: python
from matplotlib import pyplot as plt
from solo_epd_loader import epd_load
df_protons, df_electrons, energies = \
epd_load(sensor='het', level='l2', startdate=20200820,
enddate=20200821, viewing='sun',
path='/home/userxyz/solo/data/')
# plot protons and alphas
ax = df_protons.plot(logy=True, subplots=True, figsize=(20,60))
plt.show()
# plot electrons
ax = df_electrons.plot(logy=True, subplots=True, figsize=(20,60))
plt.show()
Example 3 - partly reproducing `Fig. 2 <https://www.aanda.org/articles/aa/full_html/2021/12/aa39883-20/F2.html>`_ from Gómez-Herrero et al. 2021 [#]_
-----------------------------------------------------------------------------------------------------------------------------------------------------
.. code:: python
from matplotlib import pyplot as plt
from solo_epd_loader import epd_load
import numpy as np
# set your local path here
lpath = '/home/userxyz/solo/data'
# load ept sun viewing data
df_protons_ept, df_electrons_ept, energies_ept = \
epd_load(sensor='ept', level='l2', startdate=20200708,
enddate=20200724, viewing='sun', path=lpath, autodownload=True)
# load step data
df_step, energies_step = \
epd_load(sensor='step', level='l2', startdate=20200708,
enddate=20200724, path=lpath, autodownload=True)
# change time resolution to get smoother curve (resample with mean)
resample = '60min'
fig, axs = plt.subplots(2, sharex=True, figsize=(8, 10), dpi=200)
axs[0].set_prop_cycle('color', plt.cm.Oranges_r(np.linspace(0,1,7)))
axs[1].set_prop_cycle('color', plt.cm.winter(np.linspace(0,1,7)))
# plot selection of ept electron channels
for channel in [0, 8, 16, 26]:
df_electrons_ept['Electron_Flux'][f'Electron_Flux_{channel}'].resample(resample).mean().plot(
ax = axs[0], logy=True, label='EPT '+energies_ept["Electron_Bins_Text"][channel][0])
# plot selection of step ion channels
for channel in [8, 17, 33]:
df_step[f'Magnet_Avg_Flux_{channel}'].resample(resample).mean().plot(
ax = axs[1], logy=True, label='STEP '+energies_step["Bins_Text"][channel][0])
# plot selection of ept ion channels
for channel in [6, 22, 32, 48]:
df_protons_ept['Ion_Flux'][f'Ion_Flux_{channel}'].resample(resample).mean().plot(
ax = axs[1], logy=True, label='EPT '+energies_ept["Ion_Bins_Text"][channel][0])
axs[0].set_ylim([0.3, 4e6])
axs[1].set_ylim([0.01, 5e8])
axs[0].set_ylabel("Electron flux\n"+r"(cm$^2$ sr s MeV)$^{-1}$")
axs[1].set_ylabel("Ion flux\n"+r"(cm$^2$ sr s MeV)$^{-1}$")
axs[0].legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)
axs[1].legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)
plt.subplots_adjust(hspace=0)
fig.savefig("gh2021_fig_2.png", bbox_inches = "tight")
plt.close('all')
**NB: This is just an approximate reproduction with different energy
channels, different time resolution, and different viewing direction!
Note also that the STEP data can not be used straightforwardly.**
|Figure|
Example 4 - partly reproducing `Fig. 2e <https://www.aanda.org/articles/aa/full_html/2021/12/aa40940-21/F2.html>`_ from Wimmer-Schweingruber et al. 2021 [#]_
-------------------------------------------------------------------------------------------------------------------------------------------------------------
.. code:: python
from matplotlib import pyplot as plt
from solo_epd_loader import epd_load
import datetime
import pandas as pd
# set your local path here
lpath = '/home/userxyz/solo/data'
# load data
df_protons_sun, df_electrons_sun, energies = \
epd_load(sensor='ept', level='l2', startdate=20201210,
enddate=20201211, viewing='sun',
path=lpath, autodownload=True)
df_protons_asun, df_electrons_asun, energies = \
epd_load(sensor='ept', level='l2', startdate=20201210,
enddate=20201211, viewing='asun',
path=lpath, autodownload=True)
df_protons_south, df_electrons_south, energies = \
epd_load(sensor='ept', level='l2', startdate=20201210,
enddate=20201211, viewing='south',
path=lpath, autodownload=True)
df_protons_north, df_electrons_north, energies = \
epd_load(sensor='ept', level='l2', startdate=20201210,
enddate=20201211, viewing='north',
path=lpath, autodownload=True)
# plot mean intensities of two energy channels; 'channel' defines the lower one
channel = 6
ax = pd.concat([df_electrons_sun['Electron_Flux'][f'Electron_Flux_{channel}'],
df_electrons_sun['Electron_Flux'][f'Electron_Flux_{channel+1}']],
axis=1).mean(axis=1).plot(logy=True, label='sun', color='#d62728')
ax = pd.concat([df_electrons_asun['Electron_Flux'][f'Electron_Flux_{channel}'],
df_electrons_asun['Electron_Flux'][f'Electron_Flux_{channel+1}']],
axis=1).mean(axis=1).plot(logy=True, label='asun', color='#ff7f0e')
ax = pd.concat([df_electrons_north['Electron_Flux'][f'Electron_Flux_{channel}'],
df_electrons_north['Electron_Flux'][f'Electron_Flux_{channel+1}']],
axis=1).mean(axis=1).plot(logy=True, label='north', color='#1f77b4')
ax = pd.concat([df_electrons_south['Electron_Flux'][f'Electron_Flux_{channel}'],
df_electrons_south['Electron_Flux'][f'Electron_Flux_{channel+1}']],
axis=1).mean(axis=1).plot(logy=True, label='south', color='#2ca02c')
plt.xlim([datetime.datetime(2020, 12, 10, 23, 0),
datetime.datetime(2020, 12, 11, 12, 0)])
ax.set_ylabel("Electron flux\n"+r"(cm$^2$ sr s MeV)$^{-1}$")
plt.title('EPT electrons ('+str(energies['Electron_Bins_Low_Energy'][channel])
+ '-' + str(energies['Electron_Bins_Low_Energy'][channel+2])+' MeV)')
plt.legend()
plt.show()
**NB: This is just an approximate reproduction; e.g., the channel
combination is a over-simplified approximation!** |image1|
References
----------
.. [#] First near-relativistic solar electron events observed by EPD onboard Solar Orbiter, Gómez-Herrero et al., A&A, 656 (2021) L3, https://doi.org/10.1051/0004-6361/202039883
.. [#] First year of energetic particle measurements in the inner heliosphere with Solar Orbiter’s Energetic Particle Detector, Wimmer-Schweingruber et al., A&A, 656 (2021) A22, https://doi.org/10.1051/0004-6361/202140940
.. |Figure| image:: https://github.com/jgieseler/solo-epd-loader/raw/main/examples/gh2021_fig_2.png
.. |image1| image:: https://github.com/jgieseler/solo-epd-loader/raw/main/examples/ws2021_fig_2d.png
License
-------
This project is Copyright (c) Jan Gieseler and licensed under
the terms of the BSD 3-clause license. This package is based upon
the `Openastronomy packaging guide <https://github.com/OpenAstronomy/packaging-guide>`_
which is licensed under the BSD 3-clause license. See the licenses folder for
more information.
Acknowledgements
----------------
The development of this software has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 101004159 (SERPENTINE).
Raw data
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"description": "solo-epd-loader\n===============\n\n|pypi Version| |conda version| |python version| |pytest| |codecov| |license| |zenodo doi|\n\n.. |pypi Version| image:: https://img.shields.io/pypi/v/solo-epd-loader?style=flat&logo=pypi\n :target: https://pypi.org/project/solo-epd-loader/\n.. |conda version| image:: https://img.shields.io/conda/vn/conda-forge/solo-epd-loader?style=flat&logo=anaconda\n :target: https://anaconda.org/conda-forge/solo-epd-loader/\n.. |license| image:: https://img.shields.io/conda/l/conda-forge/solo-epd-loader?style=flat\n :target: https://github.com/jgieseler/solo-epd-loader/blob/main/LICENSE.rst\n.. |python version| image:: https://img.shields.io/pypi/pyversions/solo-epd-loader?style=flat&logo=python\n.. |zenodo doi| image:: https://zenodo.org/badge/446889843.svg\n :target: https://zenodo.org/badge/latestdoi/446889843\n.. |pytest| image:: https://github.com/jgieseler/solo-epd-loader/workflows/pytest/badge.svg\n.. |codecov| image:: https://codecov.io/gh/jgieseler/solo-epd-loader/branch/main/graph/badge.svg?token=Z8dueEWqKS\n :target: https://codecov.io/gh/jgieseler/solo-epd-loader\n\nPython data loader for Solar Orbiter's (SolO) `Energetic Particle Detector (EPD) <http://espada.uah.es/epd/>`_. At the moment provides level 2 (l2) and low latency (ll) data (`more details on data levels here <http://espada.uah.es/epd/EPD_data_overview.php>`_) obtained through CDF files from ESA's `Solar Orbiter Archive (SOAR) <http://soar.esac.esa.int/soar>`_ for the following sensors:\n\n- Electron Proton Telescope (EPT)\n- High Energy Telescope (HET)\n- SupraThermal Electrons and Protons (STEP)\n\nCurrent caveats:\n\n- Only the standard ``rates`` data products are supported (i.e., no ``burst`` or ``high cadence`` data).\n- For EPT and HET, only electrons, protons and alpha particles are processed (i.e., for HET He3, He4, C, N, O, Fe are omitted at the moment).\n- For STEP, electron data needs to be calculated manually.\n- The Suprathermal Ion Spectrograph (SIS) is not yet included. \n\nDisclaimer\n----------\nThis software is provided \"as is\", with no guarantee. It is no official data source, and not officially endorsed by the corresponding instrument teams. **Please always refer to the** `official EPD data description <http://espada.uah.es/epd/EPD_data.php>`_ **before using the data!**\n\nInstallation\n------------\n\nsolo_epd_loader requires python >= 3.6.\n\nIt can be installed either from `PyPI <https://pypi.org/project/solo-epd-loader/>`_ using:\n\n.. code:: bash\n\n pip install solo-epd-loader\n\nor from `Anaconda <https://anaconda.org/conda-forge/solo-epd-loader/>`_ using:\n\n.. code:: bash\n\n conda install -c conda-forge solo-epd-loader\n\nUsage\n-----\n\nThe standard usecase is to utilize the ``epd_load`` function, which\nreturns Pandas dataframe(s) of the EPD measurements and a dictionary\ncontaining information on the energy channels.\n\n.. code:: python\n\n from solo_epd_loader import epd_load\n\n df_1, df_2, energies = epd_load(sensor, startdate, enddate=None, level='l2', viewing=None, path=None, \n autodownload=False, only_averages=False)\n\nInput\n~~~~~\n\n- ``sensor``: ``'ept'``, ``'het'``, or ``'step'`` (string)\n- ``startdate``, ``enddate``: Datetime object (e.g., ``dt.date(2021,12,31)`` or ``dt.datetime(2021,4,15)``) or integer of the form yyyymmdd with empty positions filled with zeros, e.g. ``20210415`` (if no ``enddate`` is provided, ``enddate = startdate`` will be used)\n- ``level``: ``'l2'`` or ``'ll'`` (string); defines level of data product: level 2 (``'l2'``) or low-latency (``'ll'``). By default ``'l2'``.\n- ``viewing``: ``'sun'``, ``'asun'``, ``'north'``, ``'south'``, ``'omni'`` (string) or ``None``; not\n needed for ``sensor = 'step'``. ``'omni'`` is just calculated as the average of the other four viewing directions: ``('sun'+'asun'+'north'+'south')/4``\n- ``path``: directory in which Solar Orbiter data is/should be\n organized; e.g.\u00a0``'/home/userxyz/solo/data/'`` (string). See `Data folder structure`_ for more details.\n- ``autodownload``: if ``True``, will try to download missing data files\n from SOAR (bolean)\n- ``only_averages``: If ``True``, will for STEP only return the averaged fluxes, and not the data of each of the 15 Pixels. This will reduce the memory consumption. By default ``False``.\n\nReturn\n~~~~~~\n\n- For ``sensor`` = ``'ept'`` or ``'het'``:\n\n 1. Pandas dataframe with proton fluxes and errors (for EPT also alpha\n particles) in \u2018particles / (s cm^2 sr MeV)\u2019\n 2. Pandas dataframe with electron fluxes and errors in \u2018particles /\n (s cm^2 sr MeV)\u2019\n 3. Dictionary with energy information for all particles:\n\n - String with energy channel info\n - Value of lower energy bin edge in MeV\n - Value of energy bin width in MeV\n\n- For ``sensor`` = ``'step'``:\n\n 1. Pandas dataframe with fluxes and errors in \u2018particles / (s cm^2 sr\n MeV)\u2019\n 2. Dictionary with energy information for all particles:\n\n - String with energy channel info\n - Value of lower energy bin edge in MeV\n - Value of energy bin width in MeV\n\nSupraThermal Electron Proton (STEP) sensor electron measurements\n----------------------------------------------------------------\n\nPlease note that the STEP electron measurements are not directly provided in the publically released data, but need to be calculated from them. This process is not straightforward, and the resulting data is prone to uncertainties (like contamination). **Thus it should only be used scientifically with caution! Please refer to the** `official EPD data description <http://espada.uah.es/epd/EPD_data.php>`_ **before using the data!**\n\n\nData folder structure\n---------------------\n\nThe ``path`` variable provided to the module should be the base\ndirectory where the corresponding cdf data files should be placed in\nsubdirectories. First subfolder defines the data product ``level``\n(``l2`` or ``low_latency`` at the moment), the next one the\n``instrument`` (so far only ``epd``), and finally the ``sensor``\n(``ept``, ``het`` or ``step``).\n\nFor example, the folder structure could look like this:\n``/home/userxyz/solo/data/l2/epd/het``. In this case, you should call\nthe loader with ``path='/home/userxyz/solo/data'``; i.e., the base\ndirectory for the data.\n\nYou can use the (automatic) download function described in the following\nsection to let the subfolders be created initially automatically. NB: It might\nbe that you need to run the code with *sudo* or *admin* privileges in order to\nbe able to create new folders on your system.\n\nData download within Python\n---------------------------\n\nWhile using ``epd_load()`` to obtain the data, one can choose to automatically\ndownload missing data files from `SOAR <http://soar.esac.esa.int/soar>`_\ndirectly from within python. They are saved in the folder provided by the\n``path`` argument (see above). For that, just add ``autodownload=True`` to the\nfunction call:\n\n.. code:: python\n\n from solo_epd_loader import epd_load\n\n df_protons, df_electrons, energies = \\\n epd_load(sensor='het', level='l2', startdate=20200820,\n enddate=20200821, viewing='sun',\n path='/home/userxyz/solo/data/', autodownload=True)\n\n # plot protons and alphas\n ax = df_protons.plot(logy=True, subplots=True, figsize=(20,60))\n plt.show()\n\n # plot electrons\n ax = df_electrons.plot(logy=True, subplots=True, figsize=(20,60))\n plt.show()\n\nNote: The code will always download the *latest version* of the file\navailable at SOAR. So in case a file ``V01.cdf`` is already locally\npresent, ``V02.cdf`` will be downloaded nonetheless.\n\nExample 1 - low latency data\n----------------------------\n\nExample code that loads low latency (ll) electron and proton (+alphas)\nfluxes (and errors) for EPT NORTH telescope from Apr 15 2021 to Apr 16\n2021 into two Pandas dataframes (one for protons & alphas, one for\nelectrons). In general available are \u2018sun\u2019, \u2018asun\u2019, \u2018north\u2019, \u2018south\u2019, and \u2018omni\u2019\nviewing directions for \u2018ept\u2019 and \u2018het\u2019 telescopes of SolO/EPD.\n\n.. code:: python\n\n from matplotlib import pyplot as plt\n from solo_epd_loader import epd_load\n\n df_protons, df_electrons, energies = \\\n epd_load(sensor='ept', level='ll', startdate=20210415,\n enddate=20210416, viewing='north',\n path='/home/userxyz/solo/data/')\n\n # plot protons and alphas\n ax = df_protons.plot(logy=True, subplots=True, figsize=(20,60))\n plt.show()\n\n # plot electrons\n ax = df_electrons.plot(logy=True, subplots=True, figsize=(20,60))\n plt.show()\n\nExample 2 - level 2 data\n------------------------\n\nExample code that loads level 2 (l2) electron and proton (+alphas)\nfluxes (and errors) for HET SUN telescope from Aug 20 2020 to Aug 20\n2020 into two Pandas dataframes (one for protons & alphas, one for\nelectrons).\n\n.. code:: python\n\n from matplotlib import pyplot as plt\n from solo_epd_loader import epd_load\n\n df_protons, df_electrons, energies = \\\n epd_load(sensor='het', level='l2', startdate=20200820,\n enddate=20200821, viewing='sun',\n path='/home/userxyz/solo/data/')\n\n # plot protons and alphas\n ax = df_protons.plot(logy=True, subplots=True, figsize=(20,60))\n plt.show()\n\n # plot electrons\n ax = df_electrons.plot(logy=True, subplots=True, figsize=(20,60))\n plt.show()\n\nExample 3 - partly reproducing `Fig. 2 <https://www.aanda.org/articles/aa/full_html/2021/12/aa39883-20/F2.html>`_ from G\u00f3mez-Herrero et al.\u00a02021 [#]_\n-----------------------------------------------------------------------------------------------------------------------------------------------------\n\n.. code:: python\n\n from matplotlib import pyplot as plt\n from solo_epd_loader import epd_load\n import numpy as np\n\n # set your local path here\n lpath = '/home/userxyz/solo/data'\n\n # load ept sun viewing data\n df_protons_ept, df_electrons_ept, energies_ept = \\\n epd_load(sensor='ept', level='l2', startdate=20200708, \n enddate=20200724, viewing='sun', path=lpath, autodownload=True)\n\n # load step data \n df_step, energies_step = \\\n epd_load(sensor='step', level='l2', startdate=20200708,\n enddate=20200724, path=lpath, autodownload=True)\n\n # change time resolution to get smoother curve (resample with mean)\n resample = '60min'\n\n fig, axs = plt.subplots(2, sharex=True, figsize=(8, 10), dpi=200)\n axs[0].set_prop_cycle('color', plt.cm.Oranges_r(np.linspace(0,1,7)))\n axs[1].set_prop_cycle('color', plt.cm.winter(np.linspace(0,1,7)))\n\n # plot selection of ept electron channels\n for channel in [0, 8, 16, 26]:\n df_electrons_ept['Electron_Flux'][f'Electron_Flux_{channel}'].resample(resample).mean().plot(\n ax = axs[0], logy=True, label='EPT '+energies_ept[\"Electron_Bins_Text\"][channel][0])\n\n # plot selection of step ion channels\n for channel in [8, 17, 33]:\n df_step[f'Magnet_Avg_Flux_{channel}'].resample(resample).mean().plot(\n ax = axs[1], logy=True, label='STEP '+energies_step[\"Bins_Text\"][channel][0])\n\n # plot selection of ept ion channels\n for channel in [6, 22, 32, 48]:\n df_protons_ept['Ion_Flux'][f'Ion_Flux_{channel}'].resample(resample).mean().plot(\n ax = axs[1], logy=True, label='EPT '+energies_ept[\"Ion_Bins_Text\"][channel][0])\n\n axs[0].set_ylim([0.3, 4e6])\n axs[1].set_ylim([0.01, 5e8])\n\n axs[0].set_ylabel(\"Electron flux\\n\"+r\"(cm$^2$ sr s MeV)$^{-1}$\")\n axs[1].set_ylabel(\"Ion flux\\n\"+r\"(cm$^2$ sr s MeV)$^{-1}$\")\n axs[0].legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)\n axs[1].legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)\n plt.subplots_adjust(hspace=0)\n fig.savefig(\"gh2021_fig_2.png\", bbox_inches = \"tight\")\n plt.close('all')\n\n**NB: This is just an approximate reproduction with different energy\nchannels, different time resolution, and different viewing direction!\nNote also that the STEP data can not be used straightforwardly.**\n|Figure|\n\nExample 4 - partly reproducing `Fig. 2e <https://www.aanda.org/articles/aa/full_html/2021/12/aa40940-21/F2.html>`_ from Wimmer-Schweingruber et al.\u00a02021 [#]_ \n-------------------------------------------------------------------------------------------------------------------------------------------------------------\n\n.. code:: python\n\n from matplotlib import pyplot as plt\n from solo_epd_loader import epd_load\n import datetime\n import pandas as pd\n\n # set your local path here\n lpath = '/home/userxyz/solo/data'\n\n # load data\n df_protons_sun, df_electrons_sun, energies = \\\n epd_load(sensor='ept', level='l2', startdate=20201210,\n enddate=20201211, viewing='sun',\n path=lpath, autodownload=True)\n df_protons_asun, df_electrons_asun, energies = \\\n epd_load(sensor='ept', level='l2', startdate=20201210,\n enddate=20201211, viewing='asun',\n path=lpath, autodownload=True)\n df_protons_south, df_electrons_south, energies = \\\n epd_load(sensor='ept', level='l2', startdate=20201210,\n enddate=20201211, viewing='south',\n path=lpath, autodownload=True)\n df_protons_north, df_electrons_north, energies = \\\n epd_load(sensor='ept', level='l2', startdate=20201210,\n enddate=20201211, viewing='north',\n path=lpath, autodownload=True)\n\n # plot mean intensities of two energy channels; 'channel' defines the lower one\n channel = 6\n ax = pd.concat([df_electrons_sun['Electron_Flux'][f'Electron_Flux_{channel}'],\n df_electrons_sun['Electron_Flux'][f'Electron_Flux_{channel+1}']],\n axis=1).mean(axis=1).plot(logy=True, label='sun', color='#d62728')\n ax = pd.concat([df_electrons_asun['Electron_Flux'][f'Electron_Flux_{channel}'],\n df_electrons_asun['Electron_Flux'][f'Electron_Flux_{channel+1}']],\n axis=1).mean(axis=1).plot(logy=True, label='asun', color='#ff7f0e')\n ax = pd.concat([df_electrons_north['Electron_Flux'][f'Electron_Flux_{channel}'],\n df_electrons_north['Electron_Flux'][f'Electron_Flux_{channel+1}']],\n axis=1).mean(axis=1).plot(logy=True, label='north', color='#1f77b4')\n ax = pd.concat([df_electrons_south['Electron_Flux'][f'Electron_Flux_{channel}'],\n df_electrons_south['Electron_Flux'][f'Electron_Flux_{channel+1}']],\n axis=1).mean(axis=1).plot(logy=True, label='south', color='#2ca02c')\n\n plt.xlim([datetime.datetime(2020, 12, 10, 23, 0), \n datetime.datetime(2020, 12, 11, 12, 0)])\n\n ax.set_ylabel(\"Electron flux\\n\"+r\"(cm$^2$ sr s MeV)$^{-1}$\")\n plt.title('EPT electrons ('+str(energies['Electron_Bins_Low_Energy'][channel])\n + '-' + str(energies['Electron_Bins_Low_Energy'][channel+2])+' MeV)')\n plt.legend()\n plt.show()\n\n**NB: This is just an approximate reproduction; e.g., the channel\ncombination is a over-simplified approximation!** |image1|\n\nReferences\n----------\n\n.. [#] First near-relativistic solar electron events observed by EPD onboard Solar Orbiter, G\u00f3mez-Herrero et al., A&A, 656 (2021) L3, https://doi.org/10.1051/0004-6361/202039883\n\n.. [#] First year of energetic particle measurements in the inner heliosphere with Solar Orbiter\u2019s Energetic Particle Detector, Wimmer-Schweingruber et al., A&A, 656 (2021) A22, https://doi.org/10.1051/0004-6361/202140940\n\n.. |Figure| image:: https://github.com/jgieseler/solo-epd-loader/raw/main/examples/gh2021_fig_2.png\n.. |image1| image:: https://github.com/jgieseler/solo-epd-loader/raw/main/examples/ws2021_fig_2d.png\n\nLicense\n-------\n\nThis project is Copyright (c) Jan Gieseler and licensed under\nthe terms of the BSD 3-clause license. This package is based upon\nthe `Openastronomy packaging guide <https://github.com/OpenAstronomy/packaging-guide>`_\nwhich is licensed under the BSD 3-clause license. See the licenses folder for\nmore information.\n\nAcknowledgements\n----------------\n\nThe development of this software has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 101004159 (SERPENTINE).\n",
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